A hybrid approach for search and rescue using 3DCNN and PSO

Mishra, Balmukund; Garg, Deepak; Narang, Pratik; Mishra, Vipul Kumar

doi:10.1007/s00521-020-05001-7

Cited by 7 publications

(3 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…e 3DCNN model proposed in [16] addresses a complex scene classification problem. It uses the spatial and temporal features of the video to classify scenes as helping or non-helping in natural disasters.…”

Section: Related Workmentioning

confidence: 99%

Lite-3DCNN Combined with Attention Mechanism for Complex Human Movement Recognition

Zhu

Bin

Sun

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Three-dimensional convolutional network (3DCNN) is an essential field of motion recognition research. The research work of this paper optimizes the traditional three-dimensional convolution network, introduces the self-attention mechanism, and proposes a new network model to analyze and process complex human motion videos. In this study, the average frame skipping sampling and scaling and the one-hot encoding are used for data pre-processing to retain more features in the limited data. The experimental results show that this paper innovatively designs a lightweight three-dimensional convolutional network combined with an attention mechanism framework, and the number of parameters of the model is reduced by more than 90% to only about 1.7 million. This study compared the performance of different models in different classifications and found that the model proposed in this study performed well in complex human motion video classification. Its recognition rate increased by 1%–8% compared with the C3D model.

show abstract

Section: Related Workmentioning

confidence: 99%

Lite-3DCNN Combined with Attention Mechanism for Complex Human Movement Recognition

Zhu

Bin

Sun

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…In the past few years, the use of a drone has been increasing, and it covers almost every field of surveillance, such as road safety and traffic analysis [38], crop monitoring [39], and border area surveillance [40]. Recent work has been published in this field, which uses drone surveillance using a deep learning action recognition model for search and rescue [41]. In the field of aerial action recognition, [42] proposed a disjoint multitasking approach for action detection in drone videos.…”

Section: Literaturementioning

confidence: 99%

Background Invariant Faster Motion Modeling for Drone Action Recognition

Kotecha

Garg

Mishra

et al. 2021

Drones

Self Cite

View full text Add to dashboard Cite

Visual data collected from drones has opened a new direction for surveillance applications and has recently attracted considerable attention among computer vision researchers. Due to the availability and increasing use of the drone for both public and private sectors, it is a critical futuristic technology to solve multiple surveillance problems in remote areas. One of the fundamental challenges in recognizing crowd monitoring videos’ human action is the precise modeling of an individual’s motion feature. Most state-of-the-art methods heavily rely on optical flow for motion modeling and representation, and motion modeling through optical flow is a time-consuming process. This article underlines this issue and provides a novel architecture that eliminates the dependency on optical flow. The proposed architecture uses two sub-modules, FMFM (faster motion feature modeling) and AAR (accurate action recognition), to accurately classify the aerial surveillance action. Another critical issue in aerial surveillance is a deficiency of the dataset. Out of few datasets proposed recently, most of them have multiple humans performing different actions in the same scene, such as a crowd monitoring video, and hence not suitable for directly applying to the training of action recognition models. Given this, we have proposed a novel dataset captured from top view aerial surveillance that has a good variety in terms of actors, daytime, and environment. The proposed architecture has shown the capability to be applied in different terrain as it removes the background before using the action recognition model. The proposed architecture is validated through the experiment with varying investigation levels and achieves a remarkable performance of 0.90 validation accuracy in aerial action recognition.

show abstract

“…Due to the continuous efforts of researchers and the improvement of computational capacity of computer, deep learning algorithms have become more mature and gained fast proliferation from the field of behavior recognition. In deep learning algorithm, the most famous is the CNN network and the researchers have improved the CNN to the 3D-CNN or three-stream CNNs to recognize behaviors and obtained good results [18]- [19]. In 1997, Sepp Hochreiter and Jurgen Schidhuber [20] proposed the long short-term memory network (LSTM) that belongs to the time loop network.…”

Section: Introductionmentioning

confidence: 99%

Research on Recognition of Motion Behaviors of Copepods

et al. 2020

View full text Add to dashboard Cite

The motion behaviors of copepods has important scientific research value and there is very little research on recognition of their motion behaviors simultaneously. Recognition of the basic motion behaviors of copepods using deep learning methods can greatly reduce the time cost of distinguishing and statistics, as well as achieve the purpose of improving efficiency. Based on the characteristics of motion of copepods that bring challenges to the extraction of motion fragments from raw video and the establishment of data set, such as instantaneous moving, static status most time, small-scale and high-frequency, this paper propose an improved Camshift algorithm for detection of moving targets to overcome these challenges and establish the motion behaviors image acquisition system and a standard data set of motion behaviors, which provides the experience and methods of marine zooplankton behaviors database. Finally, the LRCN network that combines the advantages of CNN and LSTM is adopted to study the impacts of different factors on the model performance, such as the number of frames of sample, preprocessing operations and sample dimensions. Experimental results show that the LRCN network has excellent potential in classification of motion behaviors of copepods, when the number of frames of sample reaches 7, the precision, recall, f1-score are 0.96, 0.95, 0.95, respectively. In addition, the rise in number of frames and preprocessing has a positive effect on the recognition, the 4D samples (image sequence) is more suitable for the LRCN model than 3D samples (trajectory image).

show abstract

A hybrid approach for search and rescue using 3DCNN and PSO

Cited by 7 publications

References 18 publications

Lite-3DCNN Combined with Attention Mechanism for Complex Human Movement Recognition

Lite-3DCNN Combined with Attention Mechanism for Complex Human Movement Recognition

Background Invariant Faster Motion Modeling for Drone Action Recognition

Research on Recognition of Motion Behaviors of Copepods

Contact Info

Product

Resources

About